question

[suqian1988]

Can the tennis-tracking mark the ball bounce position in the video?

thank you very much. Mike

[shukkkur]

@suqian1988 Hi Mike, thanks for opening the issue! Yes, it sure can. Here, if you pass --bounce=1, it should mark the bounces in blue and display them, like in the following image (unless the model failed to predict bounces 😅)

If you want all the bounces to be marked on the court, then 👇 https://github.com/ArtLabss/tennis-tracking/blob/91f0fa2ba740e9840d23c5c0bf3dbeb3b62cbe05/predict_video.py#L337-L339 the list idx stores the frame numbers where the ball touches the court. So, change this https://github.com/ArtLabss/tennis-tracking/blob/91f0fa2ba740e9840d23c5c0bf3dbeb3b62cbe05/predict_video.py#L357-L370 with this

  while True:
    ret, frame = video.read()
    if ret:
      # if coords[i] is not None:
      for loc in idx:
        center_coordinates = int(xy[loc][0]), int(xy[loc][1])
        radius = 3
        color = (255, 0, 0)
        thickness = -1
        cv2.circle(frame, center_coordinates, 10, color, thickness)
      i += 1
      output_video.write(frame)
    else:
      break

Didn't run the code myself, but should work. Feel free to reopen the issue, anytime! 😄

[suqian1988]

oh,on the small black court, right?

is there a way to mark the bounce points on the main court?

thank you so much.

Mike.

On Mon, Feb 14, 2022 at 7:11 AM Shukur Sabzaliev @.***> wrote:

@suqian1988 https://github.com/suqian1988 Hi there, thanks for opening the issue! Yes, it sure can. In README.md I included a gif image where the bounces are marked in blue, here

[image: image] https://user-images.githubusercontent.com/78250180/153860050-72db9991-b505-4e7d-bebb-253f926a419c.png

If you want all the bounces to be marked on the court, then 👇

https://github.com/ArtLabss/tennis-tracking/blob/91f0fa2ba740e9840d23c5c0bf3dbeb3b62cbe05/predict_video.py#L337-L339 the list idx stores the frame numbers where the ball touches the court. So, change this

https://github.com/ArtLabss/tennis-tracking/blob/91f0fa2ba740e9840d23c5c0bf3dbeb3b62cbe05/predict_video.py#L357-L370 with this

while True:

ret, frame = video.read()

if ret:

  # if coords[i] is not None:

  for loc in idx:

    center_coordinates = int(xy[loc][0]), int(xy[loc][1])

    radius = 3

    color = (255, 0, 0)

    thickness = -1

    cv2.circle(frame, center_coordinates, 10, color, thickness)

  i += 1

  output_video.write(frame)

else:

  break

Didn't run the code myself, but should work. Feel free to reopen the issue, anytime! 😄

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[shukkkur]

@suqian1988 No, the code provided will mark the bounces only on the real court itself. As for for marking on the black court (mini-map), it is a bit more complex. We will need the inverse matrix and will then apply perspective transform.